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      CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets

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          Abstract

          Sensitive detection of off-target effects is important for translating CRISPR-Cas9 nucleases into human therapeutics. In vitro biochemical methods for finding off-targets offer potential advantages of greater reproducibility and scalability while avoiding limitations associated with strategies that require the culture and manipulation of living cells. Here we describe CIRCLE-seq ( Circularization for In vitro Reporting of CLeavage Effects by sequencing), a highly sensitive, sequencing-efficient in vitro screening strategy that outperforms existing cell-based or biochemical approaches for identifying CRISPR-Cas9 genome-wide off-target mutations. In contrast to previously described in vitro methods, we show that CIRCLE-seq can be practiced using widely accessible next-generation sequencing technology and does not require reference genome sequence. Importantly, CIRCLE-seq can be used to identify off-target mutations associated with cell-type-specific SNPs, demonstrating the feasibility and importance of generating personalized specificity profiles. CIRCLE-seq provides the most accessible, rapid and comprehensive method for identifying genome-wide off-target mutations of CRISPR-Cas9 described to date.

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          Most cited references15

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Efficient In Vivo Genome Editing Using RNA-Guided Nucleases

            Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems have evolved in bacteria and archaea as a defense mechanism to silence foreign nucleic acids of viruses and plasmids. Recent work has shown that bacterial type II CRISPR systems can be adapted to create guide RNAs (gRNAs) capable of directing site-specific DNA cleavage by the Cas9 nuclease in vitro. Here we show that this system can function in vivo to induce targeted genetic modifications in zebrafish embryos with efficiencies comparable to those obtained using ZFNs and TALENs for the same genes. RNA-guided nucleases robustly enabled genome editing at 9 of 11 different sites tested, including two for which TALENs previously failed to induce alterations. These results demonstrate that programmable CRISPR/Cas systems provide a simple, rapid, and highly scalable method for altering genes in vivo, opening the door to using RNA-guided nucleases for genome editing in a wide range of organisms.
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              Is Open Access

              A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

              (2013)
              Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                28 June 2017
                01 May 2017
                June 2017
                29 April 2018
                : 14
                : 6
                : 607-614
                Affiliations
                [1 ]Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA 02129 USA
                [2 ]Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129 USA
                [3 ]Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129 USA
                [4 ]Department of Pathology, Harvard Medical School, Boston, MA 02115 USA
                [5 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA
                Author notes
                Correspondence should be addressed to: jjoung@ 123456mgh.harvard.edu or shengdar.tsai@ 123456stjude.org
                [6]

                Present address: Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN 38105 USA

                Article
                NIHMS866510
                10.1038/nmeth.4278
                5924695
                28459458
                2ff2fd6a-3d30-4ad9-bf68-8b759f248c73

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                Life sciences
                Life sciences

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